This will fundamentally change the way you view and manage a travel program.

Instead of seeking to minimize travel costs, you’ll be trying to maximize sales, or perhaps minimize employee turnover – by putting a travel program in place that clearly contributes to those goals.

If sales are trending down, or employee turnover is trending up, what should you, the travel manager do to help fix these problems?

Obviously, you’ll need some new lights on your dashboard – lights driven by data from Sales and HR. More importantly, you’ll need to know how to impact those non-travel metrics.

That’s where predictive analytics comes in. You need to have a data-driven understanding of how things like cabin policy and hotel tiers impact bigger, non-travel metrics like employee productivity, health and safety, and attrition.

You need to predict with confidence that by changing a variable in the travel policy, it will cost $X and improve the non-travel metric by Y%.

You’ll do this in one of two ways. If your travel program is big enough, you’ll be able to mine your own data and build these models. If your program is too small to offer enough data, you’ll depend on benchmarks and case studies from the larger firms.

3 Responses to Why Data Predicting Trumps Data Reporting

Very interesting article. I once sat down with the head of Inside Edge who provided metrics and reports to many World Series champs. Championship teams did not pay for historic analytics, but based on trends and patterns wanted to predict what was coming next. Did the catcher usually repeat his calls the second time a batter came through a lineup so if he started him with fastballs high and away, how often would he repeat the location, etc. Another interesting conversation I had was with the Packers head of analytics. They broke things down into overt and covert tenancies. Overt is tenancies that the opposing team would be aware of while covert would be things that they likely wouldn’t alter (when a QB goes into a 2 minute drill and calls the plays, what are his crutches, etc).

For travel, I would offer up that it isn’t so much “what-if I move a percent here” as predictive (though still valuable). Instead I would say that predictive has more to do with recentness and consistency. For example, if a hotel has 1000 nights for a year but 900 of them came within a 2 week span, that was a meeting – there was no consistency to that property and they likely shouldn’t be solicited for inclusion in a transient program. Also, which destinations receive consistent volumes? At what rate do your travelers support program carriers when it isn’t natural to take them (i.e. taking Delta exit Chicago to Atlanta as opposed to hub based UA or AA)? That could help determine likelihood to make changes in your program. What has been the uptick in volume for routes or destinations the past quarter? Where is your Mergers and Acquisitions accounts traveling? Where did spikes happen last quarter that weren’t consistent with either the quarter before or the same quarter last year?

Thanks so much for opening this discussion up. Would love to hear more!